Interpretable machine learning for heterogeneous treatment effect estimators with Double ML: a case of access to credit for SMEs
author
statement of authorship
Kyrylo Medianovskyi, Aidas Malakauskas, Ausrine Lakstutiene, Sadok Ben Yahia
source
publisher
journal volume number month
vol. 225
year of publication
pages
p. 2163-2172 : ill
conference name, date
27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023, 6-8 September 2023
conference location
Athens, Greece
subject term
keyword
CATE
double ML
interpretable machine learning
Partial dependence plot
ISSN
1877-0509
notes
Bibliogr.: 38 ref
The special issue 27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023, Athens, 6 September 2023 - 8 September 2023
Open Access
Open Access
scientific publication
teaduspublikatsioon
classifier
category (general)
category (sub)
kvartiil
TTÜ department
language
inglise
Medianovskyi, K., Malakauskas, A., Lakstutiene, A., Ben Yahia, S. Interpretable machine learning for heterogeneous treatment effect estimators with Double ML: a case of access to credit for SMEs // Procedia computer science (2023) vol. 225, p. 2163-2172 : ill.